412 citations · 795 across the 22 of their papers we have counts for
22 papers
ADMIL: Attention-Distilled Multiple Instance Learning for Selective Foundation Model Inference in Pathology
Duncan Stothers, Ren-Chin Wu, William Lotter
Attention-based multiple instance learning (ABMIL) using pathology foundation model embeddings is effective for slide-level tasks, but exhaustive inference requires applying a larg…
Anatomy Contextualized Adaptation of CT Foundation Models
Roshan Kenia, Stephanie L McNamara, William Lotter
CT vision-language foundation models have demonstrated promising performance across downstream tasks, but are typically trained with whole-volume representations that dilute fine-g…
SAGE: Semantic Explainability of Attention-Based Survival Models in Computational Pathology
Abdallah Lamane, Abdul Rahman Diab, Ren-Chin Wu +1
Attention-based multiple instance learning (ABMIL) is the predominant approach for slide-level prediction in computational pathology, yet its attention maps provide only local expl…
How Seemingly Inconsequential Design Choices Dictate Performance of LLMs in Pathology
Kian R. Weihrauch, Thomas A. Buckley, William Lotter +1
General-purpose large language models (LLMs) are routinely used as baselines when evaluating specialized pathology models on whole-slide images (WSIs). Because WSIs exceed contempo…
Evaluating the Impact of Medical Image Reconstruction on Downstream AI Fairness and Performance
Matteo Wohlrapp, Niklas Bubeck, Daniel Rueckert +1
AI-based image reconstruction models are increasingly deployed in clinical workflows to improve image quality from noisy data, such as low-dose X-rays or accelerated MRI scans. How…
FDA AI Search: Making FDA-Authorized AI Devices Searchable
Arun Kavishwar, William Lotter
Over 1,200 AI-enabled medical devices have received marketing authorization from the U.S. FDA, yet identifying devices suited to specific clinical needs remains challenging because…
Do Pathology Foundation Models Encode Disease Progression? A Pseudotime Analysis of Visual Representations
Pritika Vig, Ren-Chin Wu, William Lotter
Vision foundation models trained on discretely sampled images achieve strong performance on classification benchmarks, yet whether their representations encode the continuous proce…
Leveraging Foundation Models for Histological Grading in Cutaneous Squamous Cell Carcinoma using PathFMTools
Abdul Rahman Diab, Emily E. Karn, Renchin Wu +2
Despite the promise of computational pathology foundation models, adapting them to specific clinical tasks remains challenging due to the complexity of whole-slide image (WSI) proc…
Simulating Clinical AI Assistance using Multimodal LLMs: A Case Study in Diabetic Retinopathy
Nadim Barakat, William Lotter
Diabetic retinopathy (DR) is a leading cause of blindness worldwide, and AI systems can expand access to fundus photography screening. Current FDA-cleared systems primarily provide…
Beyond Diagnosis: Evaluating Multimodal LLMs for Pathology Localization in Chest Radiographs
Advait Gosai, Arun Kavishwar, Stephanie L. McNamara +4
Recent work has shown promising performance of frontier large language models (LLMs) and their multimodal counterparts in medical quizzes and diagnostic tasks, highlighting their p…
Comparing Computational Pathology Foundation Models using Representational Similarity Analysis
Vaibhav Mishra, William Lotter
Foundation models are increasingly developed in computational pathology (CPath) given their promise in facilitating many downstream tasks. While recent studies have evaluated task…
AdvDINO: Domain-Adversarial Self-Supervised Representation Learning for Spatial Proteomics
Stella Su, Marc Harary, Scott J. Rodig +1
Self-supervised learning (SSL) has emerged as a powerful approach for learning visual representations without manual annotations. However, the robustness of standard SSL methods to…
Fluoroformer: Scaling multiple instance learning to multiplexed images via attention-based channel fusion
Marc Harary, Eliezer M. Van Allen, William Lotter
Though multiple instance learning (MIL) has been a foundational strategy in computational pathology for processing whole slide images (WSIs), current approaches are designed for tr…
Representing visual classification as a linear combination of words
Shobhit Agarwal, Yevgeniy R. Semenov, William Lotter
Explainability is a longstanding challenge in deep learning, especially in high-stakes domains like healthcare. Common explainability methods highlight image regions that drive an…
Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms
Eric Wu, Kevin Wu, William Lotter
Data scarcity and class imbalance are two fundamental challenges in many machine learning applications to healthcare. Breast cancer classification in mammography exemplifies these…
Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach
William Lotter, Abdul Rahman Diab, Bryan Haslam +10
Breast cancer remains a global challenge, causing over 1 million deaths globally in 2018. To achieve earlier breast cancer detection, screening x-ray mammography is recommended by…
Conditional Infilling GANs for Data Augmentation in Mammogram Classification
Eric Wu, Kevin Wu, David Cox +1
Deep learning approaches to breast cancer detection in mammograms have recently shown promising results. However, such models are constrained by the limited size of publicly availa…
A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception
William Lotter, Gabriel Kreiman, David Cox
While deep neural networks take loose inspiration from neuroscience, it is an open question how seriously to take the analogies between artificial deep networks and biological neur…
A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification
William Lotter, Greg Sorensen, David Cox
Screening mammography is an important front-line tool for the early detection of breast cancer, and some 39 million exams are conducted each year in the United States alone. Here,…
Recurrent computations for visual pattern completion
Hanlin Tang, Martin Schrimpf, Bill Lotter +6
Making inferences from partial information constitutes a critical aspect of cognition. During visual perception, pattern completion enables recognition of poorly visible or occlude…
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
William Lotter, Gabriel Kreiman, David Cox
While great strides have been made in using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning - leveraging unlabeled examples to lea…
Unsupervised Learning of Visual Structure using Predictive Generative Networks
William Lotter, Gabriel Kreiman, David Cox
The ability to predict future states of the environment is a central pillar of intelligence. At its core, effective prediction requires an internal model of the world and an unders…